Use of road underpasses by mammals and a monitor lizard in eastern Australia and consideration of the prey‐trap hypothesis

Abstract Road networks continue to expand globally with predictable effects on ecological systems. Research into the effectiveness of road underpasses and overpasses for wildlife has been concentrated in North America and Europe. In Australia, most studies of underpasses have been of relatively short duration and without reference sites to give context to the measured rates of use. We studied 5–7 road underpasses at two locations in eastern Australia over 2–3 years, comparing camera trap detections of animals in underpasses with those at nearby forest sites. Three species of large macropod (wallabies and kangaroos) were frequently detected in the underpasses, with some underpasses traversed 1–4 times per week, and in many cases exceeded detections in the forest. The lace monitor (Varanus varius) was detected in all underpasses, often once per week during spring and summer, and infrequently in the forest. At each location, a different small macropod species, including one regionally threatened, showed a higher probability of detection in one underpass compared with several of the forest sites. The vulnerable koala (Phascolarctos cinereus) was detected infrequently in underpasses and in the adjoining forest. The short‐beaked echidna (Tachyglossus aculeatus) had a high probability of detection in a single underpass. The “prey‐trap hypothesis” postulates that predators will exhibit increased activity at underpasses as a consequence of prey being funneled. We found the red fox (Vulpes vulpes) had high activity in some underpasses. However, its activity coincided less than expected with the activity of the mammals most at risk to it. Our results provide no consistent support for the “prey‐trap hypothesis.” Instead, our study confirms the generic value of underpasses for a range of medium‐large mammals as well as one large reptile. Habitat adjoining underpasses exert a strong influence on their use and require greater consideration to maximize underpass use.


| INTRODUC TI ON
The impact of roads on wildlife populations is a global concern because these networks occur across most parts of the globe and are increasing in their density and reach (Laurance et al., 2014). Roads lead to direct mortality of animals through vehicle strike and may disrupt wildlife populations in ways that are not easily observed, such as by preventing animals from dispersing across a formerly interconnected landscape (e.g., Olsson et al., 2008;Sawyer et al., 2016). Some wildlife species are at particular risk from these types of disruptions (e.g., species unable to avoid moving vehicles; rare species with low reproductive rates; Fahrig & Rytwinski, 2009) and may require intervention. Government road agencies have responded with greater effort during the last 20 years to reduce the effects of roads on wildlife. The most frequent response has been to install fencing to exclude wildlife from the roadway (Clevenger et al., 2001) and structures to enable animals to cross safely under or over a road (Denneboom et al., 2021).
Road crossing structures for wildlife are intended to serve five objectives: reduce vehicle strike and road-kill; improve driver safety; enable wildlife to disperse to maintain gene flow; enable seasonal migration; and enable home range movements (Denneboom et al., 2021;Kusak et al., 2009;Sawyer et al., 2012;Simpson et al., 2016;Taylor & Goldingay, 2010). Structures built under the road (underpasses), which includes modified drainage culverts, have been the most widely installed and studied structures (Denneboom et al., 2021;Taylor & Goldingay, 2010). Many studies have been conducted to determine the effectiveness of these structures, with a particular focus on their frequency of use (e.g., Clevenger et al., 2001;Clevenger & Waltho, 2000;Taylor & Goldingay, 2003).
Despite reports that underpasses were used by a range of species, there is concern that studies have suffered from design limitations, with many being of relatively short duration and having no control or reference sites to indicate whether the frequency of use of the structures is more or less what could be expected if structures were functioning effectively (van der Grift et al., 2013;van der Ree et al., 2007). Future studies need to respond to these study design issues.
Wildlife underpass studies have been conducted around the world with a much greater number conducted in North America (Taylor & Goldingay, 2010). The relative lack of studies in Australia is concerning because it is regarded as a megadiverse country, with many unique fauna and a disproportionate number of extinctions (Rodrigues et al., 2014). Additional studies of underpasses in Australia will not only benefit wildlife populations in Australia but provide independent assessment of factors identified as influential elsewhere. Thirteen studies in Australia have so far investigated the use or value of underpasses to wildlife populations. While these studies have provided important insights, they have many limitations. Only two studies extended for more than 2 years (Taylor & Goldingay, 2014;van der Ree et al., 2009). Most studies investigated fewer than five underpasses (Bateman et al., 2017;Bond & Jones, 2008;Chachelle et al., 2016;Goosem et al., 2005;Harris et al., 2010;Hayes & Goldingay, 2009;Koehler & Gilmore, 2014;van der Ree et al., 2009) and were focused on a single study area. Only two studies (Chambers & Bencini, 2015;van der Ree et al., 2009) had knowledge of wildlife populations in the habitat surrounding the underpasses, while another two studies radio-tracked animals to describe their use of the underpasses and the surrounding habitat (Bateman et al., 2017;Chachelle et al., 2016). These limitations and idiosyncrasies mean that the true value of underpasses may be underappreciated and may not lead to improvements in how this mitigation measure is implemented.
The Australian studies to date enable three generalizations about species use of underpasses. Firstly, bandicoots and macropods (kangaroos and wallabies) were regular users of underpasses (Bateman et al., 2017;Bond & Jones, 2008;Chachelle et al., 2016;Chambers & Bencini, 2015;Goosem et al., 2005;Harris et al., 2010;Taylor & Goldingay, 2003). Underpass use by large macropods is particularly important because it will improve road safety for vehicles. Secondly, underpass use was dominated by mammals, reflecting the methods used (sand tracking; wildlife cameras) which favor the detection of medium-large mammals, or because studies targeted mammals by radio-tracking. Thirdly, studies that demonstrated a benefit to threatened species were highly targeted in location to where those species were abundant (Bateman et al., 2017;Dexter et al., 2016;Harris et al., 2010;van der Ree et al., 2009).
Our study builds on the above generalizations. We use a study design where, rather than attempting to compare crossing rates of the new roads with control or reference roads (e.g., Soanes et al., 2018), we compare traverses past cameras in underpasses with traverses past cameras at randomly selected locations in the adjoining forest (e.g., Andis et al., 2017). This approach overcomes issues relating to whether the target species occur at equal abundance at treatment and reference roads. We studied underpasses below newly constructed highways, commencing approximately 1.5 years after they were open to traffic. This design precludes assessment of the impact of road construction.
A common concern with underpasses is that they may operate as prey traps by allowing predators to focus their foraging to where prey are confined (Hunt et al., 1987;Little et al., 2002). This has been investigated in detail in North America (Ford & Clevenger, 2010;Martinig et al., 2020) and Europe (Mata et al., 2020), but there has been no detailed study in Australia. Red foxes (Vulpes vulpes) and feral cats (Felis catus) have been implicated in the decline of many small and medium-sized mammals (Woinarski et al., 2015). Given that these species have often been detected in underpasses in Australia (Bond & Jones, 2008;Chambers & Bencini, 2015;Goosem et al., 2005;Harris et al., 2010), there is a need to investigate whether these predators benefit from the installation of underpasses.
Our study had four aims: (i) to compare detections of different species within underpasses with detections at random sites in the forest, (ii) to identify the species that use underpasses most frequently, (iii) to investigate whether predators use underpasses to trap prey, and (iv) to investigate whether underpasses benefit threatened species. We address these aims with investigations at two locations, approximately 180 km apart, in northeast New South Wales (NSW) in eastern Australia. Including two locations increased the range of species that could potentially use the underpasses and provides a stronger basis for generalization than if conducted at one location. We used the detections to test two competing hypotheses that the detection of species would differ between the underpasses and the forest, due to either avoidance (e.g., forest-dependent species) or attraction (e.g., predators) to the underpasses, or conversely, that they would use some underpasses more frequently than others.
Uneven use of underpasses can arise due to variation in habitat suitability near underpass entrances (e.g., Chambers & Bencini, 2015;McDonald & St Clair, 2004;Ng et al., 2004 Goldingay et al., 2018). Five of the underpasses were included in this study (Table 1) consisted of four lanes, with a 9-m-wide grassy median. Following our study, the southern section was converted to a dual carriageway and the old highway was converted to a local road. KeepGuard KG680V camera after 70 weeks due to theft. This camera was of a similar size and could be set up in a similar way to the HC500. There was no evidence that this camera performed differently to the others. Underpasses U2 and U3 were monitored for 165 weeks, U1 for 120 weeks and U4 and U5 for 24-38 weeks. One camera was installed at least 10 m inside both ends of each underpass except for U2, which had a single camera installed in the middle of the underpass. Cameras were installed at 1-1.5 m high on the side wall and angled to detect animals moving along the ground. At both locations, cameras were held in locked security housings.

| Camera monitoring
Reconyx HC500 cameras (without lures) were also installed at six random forest sites at each location (three each side of the freeway) within approximately 100 m of the road and between the most west- Although no attempt was made to equalize the fields of view of the forest and underpass cameras, the width was equivalent. There was no indication that any systematic bias existed. Our objective was to characterize the number of detections past random locations in the forest. Cameras were programmed to record five images when triggered with no delay between triggers. They were set to medium-high sensitivity. These cameras and those in the underpasses were serviced approximately every 6-8 weeks to replace SD cards and batteries. Images were subsequently viewed on a computer and the species present, date and time of each record, and direction of animal movement recorded on a spreadsheet. Only medium-large mammals and a monitor lizard were detected with sufficient frequency to analyze.
The number of passes (i.e., detections) of each species was collated for each camera. A pass in an underpass was defined as movement toward or away from the camera. Occasions where animals had clearly stopped and turned around within an underpass were not included.
Successive passes by the same species in an underpass or in the forest were only scored if an arbitrary 30 min had elapsed and the animal had left the field of view or if different individuals were evident (e.g., differences in size or markings). Multiple individuals of the same species were scored if >1 was seen in the same image. The cameras at each end of an underpass did not always record the same passage of an animal. This appeared to reflect the speed of movement of individuals, which was usually much greater than past a camera in the forest. The timing of passes at each end of an underpass was matched, so passes recorded by both cameras were counted as one pass. For each underpass, we summed the number of passes of each species.

| Analysis of detection and detection hypotheses
Data were initially collated to show the number of weekly passes by each species in each underpass or at each forest site. The most frequently detected species were analyzed in detail at each location. We pooled the long-nosed bandicoot (Perameles nasuta) and the northern brown bandicoot (Isoodon macrourus) into a bandicoot group because these species can sometimes be difficult to distinguish and there were insufficient data to analyze them separately. We chose to adopt an occupancy approach (see MacKenzie et al., 2018) to analyze our data. However, rather than focus on occupancy, which is precluded here because most species were detected across most sites (underpasses and forest), we focused on the probability of detection, which can be viewed as a measure of habitat use. We constructed weekly detection histories of our species, indicating detected (1) or not detected (0) across sites for the total duration of monitoring of underpasses and forest sites. Occupancy modeling can handle missing values (−) which in this case occurred when cameras malfunctioned, were not set properly or were stolen, or when underpass and forest sites were not surveyed concurrently.
For each species, we tested three hypotheses: (i) that the probability of detection differed between the forest and the underpass sites; (ii) that species are patchy in their use of the landscape so that the probability of detection would be high or low (i.e., patchy) at individual sites rather than similar across underpasses or across forest sites; and (iii) that the probability of detection was equivalent across all sites (i.e., a null model).
We used single-season occupancy modeling implemented within program Presence version 12.24 (USGS Patuxent Wildlife Research Centre, Laurel MD, USA). With one exception, we did not examine temporal variation in detection because the timing of forest monitoring was less extensive and not fully aligned with underpass monitoring. The exception was the lace monitor (Varanus varius), which was not present during the cooler months of the year. To account for this, we fitted a detection model that included season, which contrasted autumn and winter with spring and summer.
We started our modeling with a null model that estimated detection as equal across sites. We then fitted an "underpass" model, which estimated detection at underpass sites as different to that at forest sites. A model was then fitted to represent the "patchy detection" hypothesis. An initial model was fitted with a different covariate (i.e., dummy variable) for every site. Many sites had similar estimates of detection (overlapping standard errors), so a further model was fitted with a reduced number of site covariates. We compared models using AIC c , which is the Akaike Information Criterion corrected for small sample size (Burnham & Anderson, 2004). Models were ranked from lowest to highest AIC c . Models where ∆AIC c <2 were considered equally plausible to explain the data. Models where ∆AIC c was  >10 had essentially no support to explain the data. For many of the species, the occupancy parameter estimate converged on one so was fixed at 1.0 to ensure model convergence.

| Tests of the prey-trap hypothesis
The predators of interest were the red fox (Vulpes vulpes), the feral cat (Felis catus), and the dingo/dog (Canis familiaris). The lace monitor, a large predatory lizard, was also present but its diurnal activity meant it posed little threat to the mammals using the underpasses.
The fauna underpass prey-trap hypothesis proposes that underpasses concentrate the movement of fauna to confined and predictable locations that could be readily exploited by predators (Hunt et al., 1987;Little et al., 2002;Martinig et al., 2020). This hypothesis gave rise to a number of predictions that we tested in our study areas: (1) predators should be detected more frequently at underpasses than in the forest, (2) predators should focus their activity at underpasses where potential prey are more frequently detected, and (3) the temporal use of underpasses by predators and potential prey should not be independent. We assumed that predators are highly responsive to prey and that the underpasses are equally accessible to predators and prey. We also assumed that if predators target prey, the detection of predators in underpasses would reflect that rather than predators increasing their activity outside the entrances to the underpasses where they could not be detected. Prediction 1 was tested by comparing the detection models described above.
This prediction would be supported if a model that contrasted underpasses with forest sites fit the data better than the patchy or null model and detection was higher in the underpasses compared with the forest. Prediction 2 was tested using a descriptive approach due to the small number of underpasses where predators had high levels of activity. We compared the estimates of the probability of detection of predator and prey for these underpasses. We also compared a sum of the total number of detections of predators and prey in these underpasses. This prediction would be supported if predator activity aligned with underpasses with the highest prey activity.
To test the third prediction, we collated temporal data on underpass use only for those underpasses where both prey and predators were frequently detected. This restriction was imposed so that the outcome was not determined by sparse data. The data collated were the number of nights in which a predator (a), a prey (b), and both predator and prey co-detection was independent). The binomial test calculates a onetailed significance probability of obtaining the observed proportion or a more extreme proportion by chance (Zar, 2009).

| Port Macquarie
Excluding the predator species, there were 3476 detections of native mammals and one reptile in the underpasses at Port ( Table 2).
The swamp wallaby (Wallabia bicolor) (Figure 4a The red fox was the most commonly detected predator, using one underpass more than once per week (Table 2). Only a single feral cat was detected across all sites.

| Grafton
Excluding the predatory species, there were 1408 detections of native mammals and one reptile in the underpasses at Grafton (Table 2). The rufous bettong (Aepyprymnus rufescens) (Figures 4g   and 5d), a regionally threatened species, was detected in one underpass almost once per week, which was more frequent than its detection at any of the forest sites. The lace monitor was detected in all underpasses and in four more than once per week. Its use of the underpasses greatly exceeded its detection at the forest sites.
The echidna (Figures 4h and 5e) was detected in one underpass at a higher rate than at any of the forest sites. The bandicoots were detected in two underpasses at levels greater than or equivalent to that at five of the forest sites. The larger macropods (Figure 4i) were detected in many of the underpasses but at a lower frequency compared with the forest. The koala was detected only once in an underpass. Common brushtail possums (Trichosurus vulpecula) (Figure 5f) were detected at four of the forest sites but only in one underpass.
Water rats (Hydromys chrysogaster) were detected on five occasions in the underpasses but never in the forest. The three mammalian predators were detected in most underpasses but at a relatively low frequency of mostly <10 times per year ( Table 2). The fox and feral cat were rarely detected in the forest, whereas the dingo was detected at the forest and underpass sites at an equivalent frequency.

| Probability of detection at Port Macquarie and Grafton
There was no support for a null model or a model contrasting underpass and forest sites for any species at either Port (Table 3) or Grafton (Table 4). In every case, a model which allowed detection to differ among a reduced set of sites had the greatest support. These models revealed that for many species, the probability of detection (hereafter detection) in at least one underpass exceeded detection at several of the forest sites (Figures 6 and 7). Detection of the swamp wallaby at Port was very high in three of the underpasses It was detected infrequently in the underpasses but at high levels at some forest sites (Figure 7e,f). The echidna was detected infrequently at Port (Table 2) but more frequently at Grafton, including in one underpass (Figure 7g).

| Predators should be detected more frequently at underpasses than in the forest
In all cases, the patchy site detection model showed the best fit to the predator data (Tables 2 and 3 at Grafton with greater variation observed in the forest. The red fox (Figure 8b) was only detected at three of the underpasses at Port and not in the forest (Figure 9c). Its apparent absence at two underpasses led to the patchy model being favored. At Grafton, its probability of detection was low overall but higher at two of the underpasses (Figure 9c,d). The feral cat (Figure 8c) was detected only findings provide only partial support for the first prey-trap prediction that detection would be higher at the underpasses, and only in relation to the fox. Given the dingo and feral cat were detected infrequently, they have not been considered in the other predictions.

| Predators should focus their activity
at underpasses where potential prey are more frequently detected At Port, small macropods had a very high probability of detection at U1 (Figure 6g), and bandicoots and medium-sized macropods had a very high probability of detection at U2 (Figure 6a,c). The fox had its highest probability of detection at U3 (Figure 9c). Although the swamp wallaby also had a high probability of detection at U3, other potential prey had low levels of detection at U3. Summing the weekly activity (Table 2) of potential prey (swamp wallaby, red-necked wallaby, bandicoots, pademelon) showed that activity of these prey in U2 (336 detections per year [D/Y]) was 2.9 times higher than their activity in U3 (115 D/Y) and their activity in U1 was 2.1 times higher than in U3. This suggests that foxes did not respond as predicted to underpasses with the highest prey activity.
At Grafton, the red fox was most likely to be detected ( Figure 9) and had its highest level of activity in U2 (Table 2). This underpass had the highest level of prey activity (68 D/Y), primarily due to the rufuous bettong, and one detection was of a fox carrying a bettong (Figure 8b).
Therefore, although foxes were detected where prey abundance was highest, there appeared to be little targeting of other underpasses.

| The temporal use of underpasses by
predators and potential prey should show a significant association if predators align their activity to exploit prey using the underpasses Sufficient data were available to conduct six tests encompassing three underpasses at Port and one underpass at Grafton (Table 5). The prey group involving the smaller species was dominated by pademelons in one underpass and bandicoots in another. At Grafton, this group was dominated by bettongs. In all seven tests, involving the two differentsized prey groups, the observed proportion of nights in which both predators and prey were detected was significantly lower than the proportion expected. This result enables the prediction to be rejected.

| Use of underpasses
Our study has provided a more detailed examination of underpass use by Australian wildlife than any previous study. It was conducted at two widely spaced locations, extended for 2-3 years, compared underpass detections with those at random forest sites, and evaluated TA B L E 3 Model selection results of weekly detection at Port Macquarie at underpass and forest sites. Variables in the models conditioned detection on whether a site was an underpass or forest (underpass) that some sites were different and some were equivalent (patchy), or sites were all equivalent (null). For the lace monitor, "season" contrasts spring and summer with autumn and winter.  showed frequent use of one underpass, with detections exceeding that at many of the forest sites. These species, the red-necked pademelon and the rufous bettong, have not been documented using underpasses previously. However, this finding is not surprising given that regular use of some underpasses by small macropods has been documented before (Bateman et al., 2017). One interesting finding not documented previously was the frequent use of an underpass by the echidna. Overall, these findings confirm the generic value of underpasses and that the more underpasses and locations that are studied the more species that will be detected using underpasses.

| Detections in underpasses versus in the forest
We detected five species of macropod, two species of bandicoot, the echidna and a monitor lizard using some of the underpasses with high frequency (>0.5 times per week). Our monitoring within the adjoining forest revealed that the probability of detection in the underpasses often exceeded that at some of the forest sites, suggesting the underpasses were favored for use, and also that some of the variation in use is a consequence of habitat heterogeneity.
There was no indication that camera placement led to systematically reduced detections in the forest because all species showed variation in detection across the underpasses as well as across the forest, and some species had very high detection at some forest cameras compared with many of the underpasses. The exclusion fencing that was present may also have led to animals being funneled. Andis et al. (2017) adopted a similar approach of comparing detections in underpasses with those in the adjoining habitat. They highlighted TA B L E 4 Model selection results of weekly detection at Grafton at underpass and forest sites. Variables in the models conditioned detection on whether a site was an underpass or forest (underpass) that some sites were different and some were equivalent (patchy), or sites were all equivalent (null). The lace monitor also included a "season" covariate. W-model weight; K-number of parameters

TA B L E 4 (Continued)
the value of this control-impact design to control for spatial variability in detections. They found that deer and carnivores were detected more often moving through the underpasses compared with the adjoining habitat and that there was substantial variation across individual structures. They found only one species, the coyote, to be detected less often in underpasses compared with the natural habitat. We found the brushtail possum, which encompassed two species, was detected infrequently in the underpasses whereas it was detected at a high frequency at some of the forest sites. This is consistent with the observation of infrequent use of underpasses by these species in earlier studies (Bond & Jones, 2008;Chambers & Bencini, 2015;Taylor & Goldingay, 2003). This requires further investigation to understand why brushtail possums may be reluctant to use these underpasses. However, road crossing structures are not simply intended to allow foraging movements on either side of a road but also need to enable dispersal movements and gene flow across a landscape, and therefore infrequent use (i.e., detections) may still indicate an effective structure.
Given the importance that adjacent habitat can play in underpass use (see Clevenger & Waltho, 2000;McDonald & St Clair, 2004;Ng et al., 2004), the question arises whether underpasses should be more purposely located or whether habitat restoration at underpass entrances should be more purposeful. Currently in eastern Australia, habitat restoration near underpasses is generally minimal and mostly to do with erosion control. Consideration has been given to provide structures within underpasses (e.g., Goldingay et al., 2019;Goosem et al., 2005;Mansergh & Scotts, 1989), but much less attention has been given to restoring habitat outside underpasses to cater for the F I G U R E 6 Probability of weekly detection (mean + SE) of different macropod species within underpasses and at random forest sites at Port Macquarie (left panels) and Grafton (right panels). Note the y-axis scale differs across panels

| Threatened species
In Australia, wildlife road crossing structures are often installed for threatened species (e.g., Mansergh & Scotts, 1989). In eastern Australia, concern about the impacts of new roads on the threatened koala has been responsible for the installation of vast lengths of road-side fencing and large numbers of underpasses (e.g., Lunney et al., 2022;Taylor & Goldingay, 2003). Our study provides some insight into the effectiveness of this strategy for the koala and other threatened species. We found koalas used underpasses very infrequently, but detections were also infrequent in the adjoining forest. At Port, the koala population was reduced after road construction because, prior to road clearing, nine koalas were captured and translocated 7.5 km away (Phillips, 2018 Our study detected other threatened species using the underpasses. The regionally threatened rufous bettong was detected frequently in one underpass at Grafton, despite relatively infrequent detections in the adjoining forest. We detected the regionally threatened brush-tailed phascogale (Phascogale tapoatafa) once in an underpass but also only once in the forest. The endangered spotted-tailed quoll (Dasyurus maculatus) was detected once in the forest at each study location but never in an underpass. The wide range of species detected in the underpasses suggests threatened species will benefit. These species usually occur at low density, so it is expected they would be detected infrequently within an underpass. The conclusion we draw is that underpasses are likely to assist in facilitating gene flow for many different threatened species and, therefore, assist their conservation.

| Tests of the prey-trap hypothesis
Our study provides the first comprehensive investigation of the prey-trap hypothesis for wildlife underpasses in Australia. Previous studies in Australia present either anecdotal or circumstantial accounts (Harris et al., 2010;Hunt et al., 1987). Our analysis, which involved multiple underpasses monitored over >2 years in two independent landscapes, and several species that are commonly preyed on by predators, suggests these underpasses did not act as prey traps. That does not mean that predators at individual underpasses elsewhere will not have an adverse impact on prey species (e.g., Harris et al., 2010). The main prey species in the present study were macropods, which have an ability to move rapidly through open habitats, so may not be at any greater risk to predators in underpasses.
We made several predictions from the prey-trap hypothesis including that predators would be detected more often in the underpasses than in the forest. Only the red fox was consistently detected more frequently in underpasses compared with the forest. We predicted predators would target underpasses where potential prey were most common. There was only partial support for this. At Port, the red fox showed its highest level of activity in U3 (with a concrete floor) but activity of potential prey was 2.9 times higher in U2. At Grafton, the red fox had its highest level of activity in U2 (with an earthen floor), which had the highest level of prey activity, but foxes were rarely detected in other underpasses where prey activity was relatively high. Underpass features such as floor type do not seem to be influential. These observations suggest foxes may have been influenced by landscape features rather than prey activity alone.
The most definitive test of the prey-trap hypothesis tested the prediction that predators should align the timing of their use of underpasses to match that of their prey. We conducted seven tests of this prediction using data from different underpasses and involving different prey groups. The central tenet of this assessment is that if predators and prey use the same underpass independently of each other, the proportion of nights when they are both detected will reflect the proportion of nights they individually use the underpass.
Deviation from that may reflect either avoidance by the prey or targeting by the predator. In every case, we found that the proportion of nights when both predators (red foxes) and prey were detected in underpasses was significantly lower than expected. This potentially suggests that prey show some avoidance of the predator. A limitation of this analysis is that we assumed that detection of foxes within an underpass reflects their hunting intention. It is possible that they may have hunted immediately outside an underpass where they could not be detected by our cameras. However, Harris et al. (2010) found a significant correlation between daily red fox activity and bandicoot activity within an underpass, and subsequently bandicoots disappeared from the underpass. Future studies could also place cameras immediately surrounding underpass entrances to test the above assumption.
Our study builds on evidence from other systems, which provide limited support for the prey-trap hypothesis. Ford and Clevenger (2010) used camera detections of ungulates and carnivores in Canada to compare the intervals between prey-predator sequences and predator-prey sequences. They predicted that if predators actively pursue prey the prey-predator interval should be shorter than the predator-prey interval, whereas the converse would indicate avoidance of underpasses by prey due to predator activity. If predators and prey use underpasses independently, then these intervals should be about equal. Their data supported the notion that predators and prey used the underpasses independently. Martinig et al. (2020) tested camera-detection sequences through underpasses of small mammal prey and predators and found shorter intervals and greater frequency of prey-prey sequences compared with prey-predator sequences, which was contrary to their predictions based on the prey-trap hypothesis. They suggested deviation from predictions in their Canadian system may arise because prey species were more abundant in the surrounding landscape compared with the predators. Mata et al. (2020) used co-occurrence modeling to analyze data from tracking strips at underpasses and overpasses in Spain for three prey groups (mouse-sized up to lagomorphs) and five predator types (mustelids up to large canids). They found evidence of avoidance of predators by some prey as well as some positive associations, suggesting predators targeted their prey. This varied result may suggest differences in morphology and ecology play a role, and that it is too simplistic to expect a single response across all predator-prey pairs. Caldwell and Klip (2020) found evidence in underpasses in California of prey avoidance of predators as well as predators favoring locations and times of higher prey activity.
These findings suggest that prey may be very responsive to their predators, and to use underpasses where complex habitats are not available may require predator avoidance. The small number of detailed studies into the prey-trap hypothesis suggests this hypothesis is not universally applicable. Some prey groups may be more vulnerable to predators than others, such as those that rely on habitat cover to avoid predators (e.g., McDonald & St Clair, 2004). These studies provide evidence that prey may exhibit avoidance behavior to reduce their risk. This is not surprising given that predators will provide scent cues to which prey can respond (see Kats & Dill, 1998). Further study of the prey-trap hypothesis is warranted. Providing habitat complexity within underpasses (e.g., refuges) is a management response that may alleviate the risk of predation for some species.

We thank the New South Wales Roads & Maritime Services
(Transport for NSW) for funding the field projects that are the basis of this study. The views expressed here are those of the authors. This research was conducted with the approval of the Southern Cross University animal ethics committee. We thank three anonymous reviewers whose comments helped improve this paper. We thank Craig Taylor for assistance with equipment at Port Macquarie and Nick Priest for assistance with surveys at Grafton. Luke Andrews is thanked for producing Figure 1.

CO N FLI C T O F I NTE R E S T
The authors have no conflicts of interest to declare.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data used for analysis in this study are accessible at Dryad: https:// doi.org/10.5061/dryad.cvdnc jt6m.